Transducers are commonly used in patient monitoring to gather information about a patient's condition. The resulting signals are often a mixture of physiological phenomena. In general, this is an inherent property in all electrical and pressure signals derived from mechanical movement in the body, in particular, organ movement such as that by the diaphragm and the heart. Unfortunately, the intrinsic characteristics of these measured signals are often overwhelmed by the strength of the artifacts introduced by the heart. Of the organs, the heart usually has the most pronounced and most rapid motion, which can be picked up very easily by the transducers when measuring another phenomenon. In other words, the electrical signals from the heart often "drown out" the signals of interest. For example, respiratory impedance is a mixture of electrical changes due to respiration and electrical changes due to mechanical cardiac events. Observing each phenomena in isolation is desirable for medical analysis and patient monitoring.
One technique to isolate the phenomena is fixed-frequency filtering. This method has limited success since the frequency ranges overlap for heart and respiration rates. As a result, removing the cardiac artifacts at fixed frequencies often fails altogether or significantly distorts the filtered respiration signal. Since the physiological artifacts, such as those due to cardiac or other muscle activity, vary in time with response to stress and illness, fixed-frequency filtering is ineffective at artifact removal.
In "Canceling the Cardiogenic Artifact in Impedance Pneumography", IEEE/Seventh Annual Conference of the Engineering in Medicine and Biology Society, pp 855-859, Sahakian et al attempted adaptive filtering by applying a cardiac artifact template which was then subtracted from the respiratory impedance (RI) waveform. The timing signal for adapting the signal averaged cardiac artifact template was the output of a conventional QRS detector. The QRS detections further provided the pacing for subtracting the cardiac artifact template from the RI waveform. This technique failed to take into account the beat-to-beat variations in the amplitude and shape of the cardiac artifact: the shape of the current cardiac artifact is often significantly different from that of the signal-averaged cardiac artifact, so that simply subtracting the averaged cardiac artifact is often ineffective and may even introduce new artifacts. Furthermore, template-adaptive filtering is not responsive enough to remove beat-to-beat variations.
In the "Elimination of Breathing Artifacts from Impedance Cardiograms at Rest and During Exercise", Medical and Biological Engineering & Computing, January 1988, pp 13-16, Eiken and Segerhammer experimentally reduced breathing artifacts contained in an impedance cardiogram by using a moving-window technique in conjunction with linear regression analysis. The window length had a width that was jump adapted at the start of each new cardiac cycle to be equal to the length of the previous cardiac cycle. Using the sample points within this window, they then performed a linear regression to find a straight line segment that was the "best" straight line approximation to the data within the window. Finally, the center point of the regression line was used to estimate the value of the respiration signal with the cardiac artifact removed. The window length was updated and a linear regression analysis was performed when the center sample passed the R-beat. This technique was inadequate for two reasons: linear regression is computationally expensive and jump adaptation introduces artifacts into the resulting filter output.